Duy-Dinh Le

69 papers receiving 539 citations

Peers

Duy-Dinh Le
Comparison fields: 5 of 93
  • Computer Vision and Pattern Recognition 407
  • Artificial Intelligence 123
  • Electrical and Electronic Engineering 63
  • Computer Networks and Communications 46
  • Signal Processing 36
Replace Haoji Hu with:
Haoji Hu China
Miroslav Benčo Slovakia
Ning Xu China
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Rin-ichiro Taniguchi Japan
Yuting Yang China
Baoyuan Liu United States
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Çağlar Aytekin Finland
Duy-Dinh Le relative to Haoji Hu China Haoji Hu's profile →
Citations per field
00.5×10.3×
Haoji Hu · 1×
Citations per year

Countries citing papers authored by Duy-Dinh Le

Since Specialization
Citations

This map shows the geographic impact of Duy-Dinh Le's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Duy-Dinh Le with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Duy-Dinh Le more than expected).

Fields of papers citing papers by Duy-Dinh Le

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Duy-Dinh Le. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Duy-Dinh Le. The network helps show where Duy-Dinh Le may publish in the future.

Co-authorship network of co-authors of Duy-Dinh Le

This figure shows the co-authorship network connecting the top 25 collaborators of Duy-Dinh Le. A scholar is included among the top collaborators of Duy-Dinh Le based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Duy-Dinh Le. Duy-Dinh Le is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
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3
NII Hitachi UIT at TRECVID 2019.
0
4 7
5
NII-HITACHI-UIT at TRECVID 2016.
4
6
Video Event Detection by Exploiting Word Dependencies from Image Captions
1
7
NII-UIT at MediaEval 2016 Predicting Media Interestingness Task.
2
8 4
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NII-UIT at MediaEval 2015 Affective Impact of Movies Task
20
10 10
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NII-UIT at MediaEval 2014 violent scenes detection affect task
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NII, Japan at MediaEval 2011 violent scenes detection task
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13 3
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NTT Communication Science Laboratories and NII in TRECVID 2010 Instance Search Task
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15 0
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NII-ISM, Japan at TRECVID 2007: High Level Feature Extraction
4
17
Concept Detection Using Local Binary Patterns and SVM.
2
18
A Multi-Stage Approach to Fast Face Detection(Image Recognition, Computer Vision)
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19 6
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Person X Detector.
3

About Duy-Dinh Le

Duy-Dinh Le is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence, having authored 75 papers that have together received 562 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (35 papers), Face and Expression Recognition (19 papers) and Video Analysis and Summarization (18 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (407 citations), Artificial Intelligence (123 citations) and Media Technology (32 citations). Duy-Dinh Le has collaborated with scholars based in Japan, Vietnam and United States. Frequent co-authors include Shin’ichi Satoh, Thanh Duc Ngo, Tien Do, Kien Nguyen, Yusheng Ji, Shigeki Yamada, Khang Nguyen, Tam Nguyen, Michael E. Houle and Bac Le. Their work appears in journals such as IEEE Access, IEEE Transactions on Circuits and Systems for Video Technology and Pattern Recognition Letters.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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